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[null,"1.","Record Nr.",null,"UNISA996464393403316"]
[null,null,"Titolo",null,"PRICAI 2021 : trends in artificial intelligence : 18th Pacific Rim international conference on artificial intelligence, PRICAI 2021, Hanoi, Vietnam, November 8-12, 2021 proceedings / / edited by Duc Nghia Pham [and three others]"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2021]"]
[{"$nulls":4},"©2021"]
[null,null,"ISBN",null,"3-030-89370-7"]
[null,null,"Descrizione fisica",null,"1 online resource (457 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science Ser. ; ; v.13033"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part III -- Reinforcement Learning -- Consistency Regularization for Ensemble Model Based Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Method -- 4.1 Model Discrepancy and Consistency -- 4.2 Model Learning -- 4.3 Implementation -- 5 Experiments -- 5.1 Comparative Evaluation -- 5.2 Effects of Consistency Regularization -- 5.3 Ablation Study -- 6 Conclusions -- References -- Detecting and Learning Against Unknown Opponents for Automated Negotiations -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Negotiation Settings -- 3.2 Bayes Policy Reuse -- 4 Agent Design -- 4.1 Deep Reinforcement Learning Based Learning Module -- 4.2 Policy Reuse Mechanism -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Performance Against ANAC Winning Agents -- 5.3 New Opponent Detection and Learning -- 6 Conclusion -- References -- Diversity-Based Trajectory and Goal Selection with Hindsight Experience Replay -- 1 Introduction -- 2 Background -- 2.1 Reinforcement Learning -- 2.2 Goal-Oriented Reinforcement Learning -- 2.3 Deep Deterministic Policy Gradient -- 2.4 Determinantal Point Processes -- 3 Related Work -- 4 Methodology -- 4.1 Diversity-Based Trajectory Selection -- 4.2 Diversity-Based Goal Selection -- 5 Experiments -- 5.1 Environments -- 5.2 Training Settings -- 5.3 Benchmark Results -- 5.4 Ablation Studies -- 5.5 Time Complexity -- 6 Conclusion -- References -- Off-Policy Training for Truncated TD() Boosted Soft Actor-Critic -- 1 Introduction -- 2 Related Work -- 2.1 TD Learning and Multi-step "]
[{"$nulls":4},"Methods -- 2.2 TD() and Eligibility Traces -- 3 Preliminaries -- 3.1 MDPs and Temporal Difference Learning -- 3.2 Multi-step Algorithms and TD() -- 4 Soft Actor-Critic with Truncated TD () -- 4.1 Off-Policy Truncated TD() -- 4.2 Soft Actor-Critic with Truncated TD()."]
[{"$nulls":4},"4.3 SAC() Training -- 5 Experiments -- 5.1 Evaluation of SAC() -- 5.2 Ablation Study -- 6 Discussion -- References -- Adaptive Warm-Start MCTS in AlphaZero-Like Deep Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Warm-Start AlphaZero Self-play -- 3.1 The Algorithm Framework -- 3.2 MCTS -- 3.3 MCTS Enhancements -- 4 Adaptive Warm-Start Switch Method -- 5 Experimental Setup -- 6 Results -- 6.1 MCTS Vs MCTS Enhancements -- 6.2 Fixed I Tuning -- 6.3 Adaptive Warm-Start Switch -- 7 Discussion and Conclusion -- References -- Batch-Constraint Inverse Reinforcement Learning -- 1 Introduction -- 2 Offline Inverse Reinforcement Learning -- 3 Method -- 3.1 Feature Expectation Approximation -- 3.2 Policy Optimization with BRL -- 3.3 Batch-Constraint Inverse Reinforcement Learning Algorithm (BCIRL) -- 4 Experiments -- 4.1 Standard Control Environments -- 4.2 Gridworld Example -- 5 Conclusion -- References -- KG-RL: A Knowledge-Guided Reinforcement Learning for Massive Battle Games -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Rule-Mix -- 3.2 Plan-Extend -- 4 Experiment Setup -- 4.1 Environment -- 4.2 Human Knowledge Based Module Design -- 4.3 Experiment Settings -- 5 Experimental Results -- 5.1 Battle Game -- 5.2 Comparison of Training Process -- 5.3 Model Differences -- 5.4 The Influence of Different Decisions and Action Modules -- 5.5 Discussion -- 6 Conclusion -- References -- Vision and Perception -- A Semi-supervised Defect Detection Method Based on Image Inpainting -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Architecture -- 3.2 Loss Function -- 4 Experiments -- 4.1 Preparations -- 4.2 Implementation Details -- 4.3 Results -- 5 Conclusions -- References -- ANF: Attention-Based Noise Filtering Strategy for Unsupervised Few-Shot Classification -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Dictionary Noises."]
[{"$nulls":4},"3.2 Direct Noise Filter -- 3.3 Attention-Based Noise Filter -- 3.4 Dynamic Momentum Updating -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Experimental Results -- 4.4 Visualization of Filter Results -- 4.5 Ablation Studies -- 4.6 Traditional Feature Descriptor -- 5 Conclusions -- References -- Asymmetric Mutual Learning for Unsupervised Cross-Domain Person Re-identification -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Structure of Asymmetric Mutual Learning -- 3.2 Merging Clusters Algorithm -- 3.3 Similarity Weighted Loss -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Comparison with State-of-the-Art Methods -- 4.4 Ablation Study -- 5 Conclusion -- References -- Collaborative Positional-Motion Excitation Module for Efficient Action Recognition -- 1 Introduction -- 2 Related Work -- 2.1 Action Recognition -- 2.2 CNN-Based Approaches -- 2.3 Temporal Modeling in Action Recognition -- 2.4 Attention Mechanisms -- 3 Approach -- 3.1 Architecture of CPME -- 3.2 CPME Network -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Implementation Details -- 4.3 Improving the Baseline 2D CNN-Approach -- 4.4 Comparison with the State of the Art -- 5 Conclusion -- References -- Graph Attention Convolutional Network with Motion Tempo Enhancement for Skeleton-Based Action Recognition -- 1 Introduction -- 2 Related Work -- 2.1 GCN for Skeleton Action Recognition -- 2.2 Motion Tempo Modeling -- 3 Method -- 3.1 Multi-neighborhood Graph Attention Module -- 3.2 Motion Tempo Modeling -- 4 Experiments -- 4.1 Datasets -- 4.2 Training Details -- 4.3 Ablation "]
[{"$nulls":4},"Study -- 4.4 Comparisons with the State-of-the-Art Methods -- 5 Conclusion -- References -- Learning to Synthesize and Remove Rain Unsupervisedly -- 1 Introduction -- 2 Related Work -- 2.1 Single Image Deraining Methods -- 2.2 Rain Synthesis Methods."]
[{"$nulls":4},"2.3 Generative Adversarial Networks -- 3 SAA-CycleGAN -- 3.1 Overview -- 3.2 Deraining Process -- 3.3 Rain Synthesis Process -- 3.4 Objective Function -- 4 Experimental Results -- 4.1 Implementation Details -- 4.2 Rain Synthesis Results -- 4.3 Deraining Results -- 4.4 Ablation Study -- 5 Conclusion -- References -- Object Bounding Box-Aware Embedding for Point Cloud Instance Segmentation -- 1 Introduction -- 2 Related Work -- 2.1 Deep Learning Methods on Point Cloud -- 2.2 Instance Segmentation on Point Cloud -- 3 Method -- 3.1 Network Framework -- 3.2 Bounding Box Prediction Branch -- 3.3 Instance Segmentation Branch -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Ablation Study -- 4.3 Comparison with State-of-the-Art Approaches -- 5 Conclusion -- References -- Objects as Extreme Points -- 1 Introduction -- 1.1 Key-Point-Based Prediction -- 1.2 Dense Prediction -- 1.3 Motivation -- 2 Related Work -- 2.1 Anchor-Free Object Detection -- 2.2 Localization and Classification Spatial Misalignment -- 2.3 Regression Loss -- 3 Method -- 3.1 Positive Sampling with Dynamic Radius -- 3.2 Network Outputs -- 3.3 EIoU Loss -- 3.4 EIoU Predictor -- 3.5 Optimization -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Ablation Study -- 4.3 State-of-the-Art Comparisons -- 5 Conclusion -- References -- Occlusion-Aware Facial Expression Recognition Based Region Re-weight Network -- 1 Introduction -- 2 Related Work -- 2.1 FER Methods Against Occlusions -- 2.2 Sparse Representation -- 3 Proposed Method -- 3.1 Overview of Region Re-weight Network -- 3.2 Occlusion-Aware Module -- 3.3 Block-Loss Module -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Visualization of the Blocks Selected by OAM -- 4.4 Ablation Studies Evaluation -- 4.5 Results and Comparison -- 5 Conclusion -- References."]
[{"$nulls":4},"Online Multi-Object Tracking with Pose-Guided Object Location and Dual Self-Attention Network -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Soft-Pose-NMS Object Detection Strategy -- 3.2 Feature Extraction with Dual Self-Attention Network -- 3.3 Data Association and Trajectory Management -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Performance on MOT Benchmark Datasets -- 4.3 Ablation Studies -- 5 Conclusions -- References -- Random Walk Erasing with Attention Calibration for Action Recognition -- 1 Introduction -- 2 Related Work -- 2.1 Video Action Recognition -- 2.2 Motion Occlusion in Video -- 3 Approach -- 3.1 Network Overview -- 3.2 Random Walk Erasing Module -- 3.3 Attention Calibration Module -- 4 Experiments -- 4.1 Datasets and Implementations -- 4.2 Main Results -- 4.3 Ablation Studies -- 5 Conclusion -- References -- RGB-D Based Visual Navigation Using Direction Estimation Module -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Task Definition -- 3.2 3D Geometry -- 3.3 Visual and Spatial Features of Objects -- 3.4 Direction Estimation Module -- 3.5 Actor-Critic Policy Network -- 4 Experiment -- 4.1 Dataset and Evaluation -- 4.2 Experiment Setup and Comparison Methods -- 4.3 Training Details -- 4.4 Results and Analysis -- 4.5 Ablation Study -- 5 Conclusion -- References -- Semi-supervised Single Image Deraining with Discrete Wavelet Transform -- 1 Introduction -- 2 Related Works -- 3 Semi-supervised Image Deraining by DWT -- 3.1 Methodology Overview -- 3.2 Residual Attentive Network Architecture -- 3.3 Discriminator by DWT for Semi-supervised Method -- 4 Experimental Results -- 4.1 Datasets and Measurements -- 4.2 Implementation Details -- 4.3 Results and "]
[{"$nulls":4},"Analysis -- 4.4 Ablation Study -- 5 Conclusion -- References -- Simple Light-Weight Network for Human Pose Estimation -- 1 Introduction -- 2 Methodology."]
[{"$nulls":4},"2.1 Adaptive Convolution."]
[null,"2.","Record Nr.",null,"UNISA996659666003316"]
[null,null,"Autore",null,"ALESSI, Riccardo"]
[null,null,"Titolo",null,"Il gruppo multinazionale : il controllo unilaterale nell'ordinamento italiano / Riccardo Alessi"]
[null,null,"Pubbl/distr/stampa",null,"Firenze, : [s.n.], 1985"]
[null,null,"Edizione",null,"[Ed. provvisoria]"]
[null,null,"Descrizione fisica",null,"349 p. ; 24 cm"]
[null,null,"Disciplina",null,"346.45066"]
[null,null,"Soggetti",null,"Imprese multinazionali - Controllo - Legislazione"]
[null,null,"Collocazione",null,"XVI.7.A. 267"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]